A WordNet-based algorithm for word sense disambiguation

  • Authors:

  • Xiaobin Li;Stan Szpakowicz;Stan Matwin

  • Affiliations:

  • Department of Computer Science, Concordia University, Montreal, Quebec, Canada;Department of Computer Science, University of Ottawa, Ottawa, Ontario, Canada;Department of Computer Science, University of Ottawa, Ottawa, Ontario, Canada

  • Venue:
  • IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
  • Year:
  • 1995

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present an algorithm for automatic word sense disambiguation based on lexical knowledge contained in WordNet and on the results of surface-syntactic analysis The algorithm is part of a system that analyzes texts in order to acquire knowledge in the presence of as little pre-coded semantic knowledge as possible On the other hand, we want to make the besl use of public-domain information sources such as WordNet Rather than depend on large amounts of hand-crafted knowledge or statistical data from large corpora, we use syntactic information and information in WordNet and minimize the need for other knowledge sources in the word sense disambiguation process We propose to guide disambiguation by semantic similarity between words and heuristic rules based on this similarity The algorithm has been applied to the Canadian Income Tax Guide Test results indicate that even on a relatively small text the proposed method produces correct noun meaning more than 72% of the time.